In-silico investigation integrated with machine learning to identify potential inhibitors targeting AKT2: Key driver of cancer cell progression and metastasis.
- DOI
- 10.1016/j.cmpb.2025.108793
- Published
- 2025-04-18
- Container
- Comput Methods Programs Biomed
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1016/j.cmpb.2025.108793,
title = {In-silico investigation integrated with machine learning to identify potential inhibitors targeting AKT2: Key driver of cancer cell progression and metastasis.},
author = {Shahrior R and Tamkin S and Khan MB and Meraj AJ and Bhuiyan H.},
year = {2025},
journal = {Comput Methods Programs Biomed},
doi = {10.1016/j.cmpb.2025.108793},
url = {https://doi.org/10.1016/j.cmpb.2025.108793}
}RIS
TY - JOUR TI - In-silico investigation integrated with machine learning to identify potential inhibitors targeting AKT2: Key driver of cancer cell progression and metastasis. AU - Shahrior R AU - Tamkin S AU - Khan MB AU - Meraj AJ AU - Bhuiyan H. PY - 2025 JO - Comput Methods Programs Biomed DO - 10.1016/j.cmpb.2025.108793 UR - https://doi.org/10.1016/j.cmpb.2025.108793 ER -
APA
R, S., S, T., MB, K., AJ, M., & H., B. (2025). In-silico investigation integrated with machine learning to identify potential inhibitors targeting AKT2: Key driver of cancer cell progression and metastasis.. Comput Methods Programs Biomed. https://doi.org/10.1016/j.cmpb.2025.108793
Source records
- europe-pmc · retrieved 2026-09-25T12:26:29.311Z